Since the invention in 2016 [1]}, FL has transformed many applications by providing distributed AI solutions at the network edge with the cooperation of multiple mobile devices. Instead of offloading all raw data to a cloud or a data center to perform AI training, FL enables distributed learning by allocating AI functions, e.g., AI model training, directly to the local devices to build a shared global model at an aggregator, e.g., an MEC server at a base station (BS) or an access point (AP) [2]}. Following the recent advances in mobile hardware and increasing concerns of user privacy, FL is particularly attractive for various intelligent edge services such as smart transportation and smart healthcare [3]}. For example, in smart vehicular networks, vehicles can act as learning clients to train local models and collaborate with a road side unit (RSU) to build traffic prediction models, aiming to build a comprehensive vehicle routing map for reducing traffic congestion [4]}. As a BS cannot collect all data from distributed mobile devices for AI/ML training, FL is of paramount importance in realizing full intelligence in the next generation of mobile edge networks. FL allows devices and the BS to train a common model while raw datasets are kept locally at users where the data reside.
